\mychapterstar{Abstract}
The natural gas processing is one of the most important activities of
petrochemical industry. In a Natural Gas Processing Unit (NGPU) the liquefied
petroleum gas (LPG) is the product that has the largest economical potencial,
altough other products, such as natural gasoline and industrial quality residual
gas, are also obtained as a fractional process result.

Due to the high market competition, the companies are reducing their
expenditures with production and quantity of raw material used, without the lost
of product quality. To make this possible, it is necessary to develop efficient
control strategies.

In NGPUs, that quality control is based on the final products compositions.
However, even when this analysis is performed by specific instruments, such as
chromatographs, the measurement and purgation time of the samples hinder the
development of advanced control strategies.

The goal of this work is to develop an inferential system using artificial
neural networks (ANNs) that can estimate the ethane and pentane molar fraction
on LPG and the propane molar fraction on residual gas. The system will uses a
NGPU simulated by HYSYS\textsuperscript{\textregistered} software, composed by a
deethanizer column in series with a debuthanizer column.

The system should estimate the products molar fractions every minute, allowing
the application of multivariable control techniques in order to maintain the LPG
quality and minimize the process loss. The statistical technique of principal
Components Analysis is used to reduce the neural network complexity.

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{\bf Keywords}: Inference, Distillation Columns, Principal Components Analysis,
Artificial Neural Networks.
